Methods and systems for timing impact of nourishment consumpiion

ABSTRACT

A system for timing impact of nourishment consumption, the system including a computing device configured to receive a nutrient profile of a subject, wherein the nutrient profile maps physiological data of the subject to current nutrient levels of the subject, determine, using the nutrient profile, a nourishment consumption program, wherein the nourishment consumption program includes at least an alimentary element, and a time of day for consuming the alimentary element wherein the time of day is determined as a function of the nutrient profile and the current nutrient level of the subject, and provide, to the subject, the nourishment consumption program.

FIELD OF THE INVENTION

The present invention generally relates to the field of nutrient timing.In particular, the present invention is directed to methods and systemsfor timing impact of nourishment consumption.

BACKGROUND

The detection of the concentration level of metabolites, nutrients, andother analytes in individuals may be vitally important to their health.For example, the monitoring of glucose levels is particularly importantto individuals with diabetes or pre-diabetes. People with variousconditions may need to monitor nutrient levels to determine when, forinstance, medication is needed.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for timing impact of nourishment consumption, thesystem including a computing device configured to receive a nutrientprofile of a subject, wherein the nutrient profile maps physiologicaldata of the subject to current nutrient levels of the subject,determine, using the nutrient profile, a nourishment consumptionprogram, wherein the nourishment consumption program includes at leastan alimentary element, and a time of day for consuming the alimentaryelement wherein the time of day is determined as a function of thenutrient profile and the current nutrient level of the subject, andprovide, to the subject, the nourishment consumption program.

In another aspect, a method for timing impact of nourishmentconsumption, the method including receiving, by a computing device, anutrient profile of a subject, wherein the nutrient profile mapsphysiological data of the subject to current nutrient levels of thesubject, determining, by the computing device, using the nutrientprofile, a nourishment consumption program, wherein the nourishmentconsumption program includes at least an alimentary element, and a timeof day for consuming the alimentary element wherein the time of day isdetermined as a function of the nutrient profile and the currentnutrient level of the subject, and providing, by the computing device,to the subject, the nourishment consumption program.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating a system for timing impact ofnourishment consumption;

FIG. 2 is a diagrammatic representation of a nourishment consumptionprogram;

FIG. 3 is a diagrammatic representation of nourishment consumptionprogram on a user device;

FIG. 4 is a diagrammatic representation of a nutrient profile;

FIG. 5 is a block diagram illustrating an exemplary embodiment of amachine-learning module;

FIG. 6 is a block diagram illustrating an exemplary embodiment of anourishment program database;

FIG. 7 is a flow diagram illustrating an exemplary workflow of a methodfor timing impact of nourishment consumption; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for timing impact of nourishment consumption. In anembodiment, the system includes a computing device configured to receivephysiological data pertaining to a subject and receive a nutrientprofile. In an embodiment, computing device may determine nutrientprofile data by training a machine-learning model with physiologicaldata. Nutrient profile may include per-subject pharmacokinetics, ormetabolism, absorption, distribution, and excretion rates, for a varietyof nutrients. Computing device is configured to determine a nourishmentconsumption program to time consumption based on the nutrient profile.In an embodiment, computing device may provide compatible alimentaryelements linked to a scheduling application and use reacting computingto update nutrient profile and consumption timing at defined intervals.

Referring now to FIG. 1, an exemplary embodiment of a system 100 fortiming impact of nourishment consumption is illustrated. System includesa computing device 104. Computing device 104 may include any computingdevice as described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Computing device 104 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Computing device 104 may interface or communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting computing device 104 to one or more of a variety of networks,and one or more devices. Examples of a network interface device include,but are not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Computing device 104 may include but is not limited to, forexample, a computing device or cluster of computing devices in a firstlocation and a second computing device or cluster of computing devicesin a second location. Computing device 104 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. Computing device 104 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. Computingdevice 104 may be implemented using a “shared nothing” architecture inwhich data is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or computing device.

With continued reference to FIG. 1, computing device may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, aggregating inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. Computingdevice 104 may perform any step or sequence of steps as described inthis disclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

With continued reference to FIG. 1, computing device 104 is configuredto receive physiological data of a subject. A “physiological data,” asused in this disclosure, is chemical data, data originating from abiological extraction, medical data, and the like. A biologicalextraction may include data originating from a physical sample, such asa blood panel, lipid panel, metabolic test, genome sequencing, and thelike. Physiological data 108 may include genetic data including thepresence of single nucleotide polymorphisms (SNPs), mutations, alleledesignations (dominant, recessive, +/−, etc.), genetic sequencing data,and the like; epigenetic data including methylation patterns, geneexpression patterns, enzyme concentrations, specific activity,circulating RNAs, and the like; microbiome data including gutmicrobiota, ‘good’ flora, transient flora, opportunistic pathogens,bacteria, viruses, parasites, fungi, circulating peptides, biologics,and the like; previous medical history including surgeries, treatments,prescriptions, current and past medications, allergies, family historyof disease, diagnoses, prognoses, and the like; physiological dataincluding systolic and diastolic blood pressure, resting heart rate, VO2max, oxygen saturation, blood cell counts, hemoglobin/hematocrit levels,blood iron concentration, body mass index (BMI), blood sugar, HDL/LDLcholesterol levels, hormone levels, and the like; among any other datathat one skilled in the art may recognize as physiological data 108data. Physiological data 108 may include a variety of data, from avariety of sources, with the data originating from the subject and/or aplurality of subjects, and from a variety of categories and sources, forinstance and without limitation, as described in U.S. Nonprovisionalapplication Ser. No. 16/886,647, filed on May 28, 2020, and entitled“METHODS AND SYSTEMS FOR DETERMINING A PLURALITY OF BIOLOGICAL OUTCOMESUSING A PLURALITY OF DIMENSIONS OF PHYSIOLOGICAL DATA USER DATA ANDARTIFICIAL INTELLIGENCE,” the entirety of which is incorporated hereinby reference.

Continuing in reference to FIG. 1, physiological data 108 data maycorrespond to a nutritional need of a subject. A “nutritional need,” asused in this disclosure, is a quantity of at least a nutrient and/or ofa plurality of nutrients for a subject. Nutrient need may be recommendedfor subject for maintenance of health, improvement of physiology,addressing a symptom, disease, illness, injury, or any type of malady.Nutrient need may refer to, without limitation, macronutrients, such asprotein, including non-essential amino acids, essential amino acids,fats including non-essential fats, essential fats such as long-chainpolyunsaturated fatty acids (LC-PUFAs), short-chain polyunsaturatedfatty acids (SC-PUFAs), omega fatty acids, carbohydrates, includingdigestible and non-digestible carbohydrates such as dietary fiber,inulin, psyllium, and methylcellulose; micronutrients, such as vitaminA, thiamin (vitamin B1), riboflavin (vitamin B2), niacin (vitamin B3),pantothenic acid (vitamin B5), vitamin B6, biotin (vitamin B7), folate(vitamin B12), vitamin C, vitamin D2, vitamin D3, vitamin E, vitamin K1,vitamin K2; minerals such as calcium, phosphorous, potassium, sodium,magnesium; trace elements such as iron, sulfur, manganese, selenium,chromium, molybdenum, copper, cobalt; halides such a chloride andiodine; electrolytes and salts including bicarbonate, creatine, andphosphocreatine; caloric content, or any other substance that providesnourishment essential for growth and maintenance of subject.

Continuing in reference to FIG. 1, computing device 104 may receive anutrient profile of the subject, wherein the nutrient profile includesphysiological data 108 data mapped to current nutrient levels of thesubject. A “nutrient profile,” as used in this disclosure, is a profileincluding any nutrient data corresponding to a subject's currentnutrient levels and recommended nutrient levels. Nutrient profile 112may include subject current nutrient levels as they relate torecommended nutrient levels, for instance as numerical values theindicate the amounts relative to one another. Nutrient levels maycorrespond to blood serum levels of nutrients of current nutrition, forinstance as determined from a physiological data. Nutrient levels maycorrespond to percent daily recommended values (or recommended valuesdetermined on a customized, per-subject basis). A nutrient profile 112may include qualitative values such as “deficiency”, “surplus”, “yes”,“no”, etc., of nutrient levels. A nutrient profile 112 may includequantitative values of nutrient levels such as a numerical values,functions of values, matrices, arrays, vectors, systems of equations,variables, coefficients, metrics, parameters, and the like. A nutrientprofile 112 may serve as a “survey” of the current state of nutrition ofa subject, including any acute and chronic nutritional deficiencies,nutritional surpluses, recommended and/or calculated nutritionaltargets, etc.

Continuing in reference to FIG. 1, receiving nutrient profile 112 mayinclude generating a nutrient machine-learning model, using amachine-learning process, wherein the nutrient machine-learning model istrained with training data that includes a plurality of data entrieswherein each entry correlates physiological data 108 data to currentnutrient levels of the subject. A “machine learning process,” as used inthis disclosure, is a process that automatedly uses a body of data knownas “training data” and/or a “training set” to generate an algorithm thatwill be performed by a computing device/module to produce outputs givendata provided as inputs; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a subject and written in a programming language. A nutrientmachine-learning model 116 may be generated by training amachine-learning process, algorithm, and/or method, with training data,as described in further detail below.

Continuing in reference to FIG. 1, “training data,” as used herein, isdata containing correlations that a machine learning process may use tomodel relationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine learning processes as described in furtherdetail below.

Continuing in reference to FIG. 1, training data may includephysiological data 108. Training data may originate from the subject,for instance via a questionnaire and a user interface with computingdevice 104, providing medical history data, retrieving whole genomesequencing, and the like. Training data may be recorded and transmittedto computing device 104 via a wearable device such as a pedometer,gyrometer, accelerometer, motion tracking device, bioimpedance device,ECG/EKG/EEG data, physiological sensors, blood pressure monitor, bloodsugar and volatile organic compound (VOC) monitor, and the like.Training data may originate from an individual other than subject,including for instance a physician, lab technician, nurse, caretaker,psychologist, therapist, and the like. Training data may includebiomarkers associated with nutrient amounts and quality of subjectnutrition, such as red blood cell (RBC) count. RBC count may be elevateddue to dehydration, high testosterone. RBC count may be low due tonutrient deficiencies (iron, vitamin B6, vitamin B12, folate), kidneydysfunction, chronic inflammation, anemia, blood loss, and the like.Training data may include hemoglobin levels, which may be elevated dueto dehydration, elevated testosterone, poor oxygen deliverability,thiamin deficiency, insulin resistance. Hemoglobin levels may bedeceased due to anemia, liver disease, hypothyroidism, exercise,arginine deficiency, protein deficiency, inflammation nutrientdeficiencies (vitamin E, magnesium, zinc, copper, selenium, vitamin B6,vitamin A). Training data may include hematocrit levels which may beelevated due to dehydration, elevated testosterone, poor oxygendeliverability, thiamin deficiency, insulin resistance. Hematocritlevels may be deceased due to anemia, liver disease, hypothyroidism,exercise, arginine deficiency, protein deficiency, inflammation nutrientdeficiencies (vitamin E, magnesium, zinc, copper, selenium, vitamin B6,vitamin A). Training data may include mean corpuscular hemoglobin (MCH),or a measure of the average weight of hemoglobin per red blood cell. MCHmay be elevated (“macrocytic”) due to nutrient deficiencies (vitaminB12, folate, vitamin C), alcohol consumption, thiamin deficiency, and(falsely increased) by hyperlipidemia. MCH may be decreased(“microcytic”) due to iron deficiency, nutrient deficiencies (vitaminB6, copper, zinc, vitamin A, vitamin C). Training data may includemeasures of the average concentration of hemoglobin in red blood cells,which may be elevated (“macrocytic”) due to nutrient deficiencies(vitamin B12, folate, vitamin C), alcohol consumption, thiamindeficiency, and (falsely increased) by hyperlipidemia. Concentration ofhemoglobin may be decreased (“microcytic”) due to iron deficiency,nutrient deficiencies (vitamin B6, copper, zinc, vitamin A, vitamin C).Training data may include data on platelets or small, anucleated cellfragments in blood that are involved in clotting and important forvascular integrity. Platelets may be increased due to iron deficiencyanemia, collagen diseases, hemolytic anemia, blood loss, stress,infection, inflammation. Platelets may be decreased due to alcoholism,liver dysfunction, viral/bacterial infections, pernicious anemia,bleeding. Training data may include cellular dimension assessment, suchas measures of the average size of platelets, reflecting their function.Platelets counts may be elevated due to increased platelet production,which is often caused by loss or destruction of existing platelets.Elevated mean platelet volume (MPV) may be associated with vasculardisease and mortality, certain cancers, type 2 diabetes, and Hashimoto'sthyroiditis. MPV may be decreased due to conditions associated withunder-production of platelets such as aplastic anemia or cytotoxic drugtherapy. Training data may include red blood cell distribution width, ameasurement of the variation in red blood cell size. Typically increaseddue to nutrient deficiency-related anemias (iron, vitamin A, copper,zinc, vitamin B6). Persons skilled in the art, upon review of thisdisclosure in its entirety, the range of physiological data that mayserve as training data to determine nutrient levels of a subject.

Continuing in reference to FIG. 1, training data may include nutritionalinput data. A “nutritional input,” as used in this disclosure, is anynutritional value, nutrient amount, or the like, consumed by thesubject. A nutritional input may include any alimentary elementsconsumed by subject, over any designated period of time. An “alimentaryelement,” as used in this disclosure, is any edible element intended toprovide some nutrient value, including hydration, electrolytes,macronutrient, micronutrients, bioactive ingredients, and the like. Analimentary element may include a meal, food item, beverage, supplement,among other items. Persons skilled in the art, upon review of thisdisclosure in its entirety, the range of nutritional input data,provided by subject or otherwise, that may serve as training data, orinput data, in determining nutrient levels of a subject.

Continuing in reference to FIG. 1, computing device 104 may determinethe nutrient profile 112 as a function of the nutrient machine-learningmodel 116. For instance and without limitation, training data mayinclude subject nutritional input (and associated times of consumption)as training data to train nutrient machine-learning model to ‘learn’,based on the subject's physiological data (age, sex, height, weight,lean body mass, BMI, activity level, basal metabolism, foodintolerances, digestive issues, metabolic disorders, etc.), the effectalimentary elements have on nutrient profile 112. Nutrientmachine-learning model 116 may identify patterns in the training datathe relate to, for instance and without limitation, numerical valuesthat describe current nutrient profile categories.

Continuing in reference to FIG. 1, in non-limiting illustrativeexamples, training data may include physiological data 108 used to trainnutrient machine-learning model 116 to derive pharmacokinetics, orper-subject metabolism, absorption, distribution, and excretion rates,for a variety of nutrients and/or alimentary elements. For instance,training data may include blood concentrations (mg/dL) of nutrients(arginine, glucose, iron, etc.) after meal consumption. Persons skilledin the art may appreciate that training a nutrient machine-learningmodel with such data, over sufficiently great number of training epochs,for a variety of individual nutrients (or foods) for a variety ofalimentary element categories (grains, meats, vegetables, fruits, etc.),may result in rates at which each nutrient may increase/decrease afterconsuming a meal. In this way, training data may be used to determine anutrient profile 112 that encompasses per-subject kinetics of nutrientmetabolism, absorption, and the like, that may be used to informconsumption timing.

Continuing in reference to FIG. 1, for instance and without limitation,training data may include blood test results from blood draws by aprimary physician, or blood analyte results from a wearable device,physiological sensor, or the like. Training data may include nutrientlevels for blood analytes, such as vitamin A, glucose, magnesium, andcalcium. This data may be used to train the nutrient machine-learningmodel 116 to derive expected nutrient levels and rates of change of thenutrients (vitamin A, glucose, magnesium, and calcium), provided aninput of a consumed alimentary element. In this way, nutrient profile112 may indicate a numerical value relating to the current nutrientlevel of ‘vitamin A, glucose, magnesium, and calcium’ in a subject afterconsuming ‘breakfast’, wherein breakfast included ‘apple cinnamonoatmeal’ and ‘whole milk’. The output may include a data structure(nutrient profile 112) that may inform the timing of meals, forinstance, based on the ‘current nutrient level’, the ‘target nutrientlevel’, and ‘nutrient adsorption rates’.

Continuing in reference to FIG. 1, nutrient profile 112 data may bedetermined at regular time points and extrapolated for diets. Innon-limiting illustrative examples, measuring metabolism and/orabsorption rates may be performed using a guiding rubric, for example bylearning ‘how a ketogenic meal affects blood nutrient levels at 1 hourpost-meal, 6 hours post-meal, 24 hours post-meal, etc.’. Alternativelyor additionally, training data elements may be collected/recorded andorganized for induvial alimentary elements, for instance a fruit,vegetable, and the like. In such an example, inputs may includenutrition facts for an alimentary element, and from the relationshipsidentified (mathematically defined in the model), the expected nutrientlevel in the subject after consumption can be output. Using this outputfrom nutrient machine-learning model 116, system 100 may determine,based on how these nutrient amounts change over time, ‘when’ to plan thenext meal.

Continuing in reference to FIG. 1, computing device 104 is configured todetermine, using the nutrient profile 112, a nourishment consumptionprogram, wherein the nourishment consumption program includes at leastan alimentary element and a time of day for consuming the alimentaryelement. A “nourishment consumption program,” as used in thisdisclosure, is a plan that guides the timing of consumption of a subjectand an identity of an item for consumption. Nourishment consumptionprogram 120 may include a time of day for consuming an alimentaryelement, such as a compatible alimentary element. Nourishmentconsumption program 120 may include timing the consumption of alimentaryelements according to a threshold value of a nutrient. For instance andwithout limitation, nourishment consumption program 120 may include thetiming of meals to keep blood glucose below an upper threshold value,and above a lower threshold value. In such an example, nourishmentconsumption program 120 may include “when” subject should consume a mealto keep blood sugar within the range, wherein range may include anumerical value range. The timing of consumption may then changedepending on the alimentary element considered. For instance, ‘times’indicated in the program may be modified as a function of what isconsumed. Meals heavy in simple sugars (monosaccharides/disaccharides)may prompt the next meal to follow closely (+1-3 hours); however, if thecarbohydrate profile of a meal includes large quantities of complexsugars (starch/fiber), blood sugar may be within range for up to 4-5hours afterward. Persons skilled in the art may appreciate thatdetermining timing of consumption may be performed with a respect tovariety of goals, or targets, diets, etc. For instance, timing can beoptimized to achieve a particular amount of macromolecular nutrients(carbohydrates, fats, proteins), calories, micronutrients (iron,calcium, magnesium), to maintain a certain amount bioactive ingredient,to improve/optimize protein synthesis in muscle tissue for recovery fromexercise, adherence to a state of ketosis, and the like.

Continuing in reference to FIG. 1, determining the nourishmentconsumption program 120 may include retrieving an alimentary elementprogram comprising compatible alimentary elements. An “alimentaryelement program,” is a collection of alimentary elements provided tosubject. An alimentary element program may include compatible alimentaryelements. A “compatible alimentary element,” as used in this disclosure,is an alimentary element proscribed to a subject based on the subject'sphysiological data 108. An alimentary element program may include meals,recipes, grocery items, menu items, supplements, bioactive ingredients,beverages, and the like, that are intended for a subject based ondeterminations made from physiological data 108 data, such as foodallergies and intolerances, improving physiological state of health,decreasing inflammation, addressing chronic nutrient deficiencies, andthe like.

Continuing in reference to FIG. 1, compatible alimentary element mayinclude alimentary elements intended to address a nutrition deficiency,reduce inflammation, improve recovery from exercise, improve overallhealth, among other targeted effects. A compatible alimentary elementmay include alimentary elements provided as a function of anindividual's allergies, food intolerances, philosophical, religions, andlifestyle considerations, among other factors involved. Compatiblealimentary element may be generated and provided to a user as a functionof a physiological data, such as blood chemistry results, includingenzyme concentrations and specific activities for instance offibrinogen, ferritin, serum amyloid A, α-1-acid glycoprotein,ceruloplasmin, hepcidin, haptoglobin, tumor necrosis factor-α (TNF-α),among other acute phase proteins; for instance cytokine identities andconcentrations for instance interleukin-6 (IL-6); blood metabolitesidentities and concentrations such as blood sugar, LDL and HDLcholesterol content; hormone identities and concentrations such asinsulin, androgens, cortisol, thyroid hormones, and the like;erythrocyte sedimentation rate, blood cell counts, plasma viscosity, andother biochemical, biophysical, and physiological properties regardingblood panels, blood tests, and the like, as it relates to biomarkers ofinflammation. Compatible alimentary elements may be recommended to auser as a function of these biochemical data with the intention ofmodifying the biochemical data, for instance by modulating blood sugar,decreasing LDL cholesterol levels, reducing pro-inflammatory biomarkers,minimizing free radicals and oxidative damage, among other targetedeffects of alimentary elements on physiological data. For instance,biomarkers of inflammation may include biochemical properties specificto a user such as the level of inflammation as evidence by the presenceand concentration of inflammatory biomarkers, post-translationalmodification of proteins, epigenetic markers, etc., and alimentaryelements may be identified and provided to a subject to focus onreducing inflammation for instance and without limitation, as describedin U.S. Nonprovisional application Ser. No. 17/007,251 filed Aug. 31,2020 titled “METHOD AND SYSTEM FOR REVERSING INFLAMMATION IN A USER,”the entirety of which is incorporated herein by reference. The level ofinflammation, or any biochemical ailment and/or property of a subjectmay be enumerated, and based on the numerical value, an alimentaryelement may be recommended to the subject, from which a nourishmentconsumption program may be defined. Alternatively or additionally,determining nourishment consumption program that improves the user'shealth state based on the user's biochemistry may be performed, forinstance and without limitation, as described in U.S. Nonprovisionalapplication Ser. No. 16/375,303 filed Apr. 4, 2020 titled “SYSTEMS ANDMETHODS FOR GENERATING ALIMENTARY INSTRUCTION SETS BASED ON VIBRANTCONSTITUTION GUIDANCE,” the entirety of which is incorporated herein byreference.

Continuing in reference to FIG. 1, determining the nourishmentconsumption program 120 may include identifying a compatible alimentaryelement to address a datum of the nutrient profile 112. Nutrient profile112 may include a variety of data based on physiological data 108, asdescribed above, such as current nutrient levels, rates of metabolism,adsorption, and nutrient threshold values. A “nutrient threshold,” asused in this disclosure, is a numerical value of a nutrient. Innon-limiting illustrative examples, nutrient profile 112 may includecurrent levels of water-soluble vitamins, fat-soluble vitamins,minerals, trace elements, blood sugar, cholesterol, lipids, amino acids,phosphocreatine, calories, ATP, rates of extracting each of thesenutrients from alimentary elements, and the maximal and minimal nutrientthresholds the subject should maintain. With such data, computing device104 may identify compatible alimentary elements that may bring subjectwithin the nutrient threshold values and may determine the times of dayto initiate consumption of alimentary elements to stay within nutrientthresholds ranges throughout the day-night cycle.

Continuing in reference to FIG. 1, nourishment consumption program 120may be used to identify a compatible alimentary element to, forinstance, address a nutrient deficiency. In non-limiting illustrativeexamples, computing device 104 may compare nutrient levels in nutrientprofile 112 to recommended daily allowance indicated by alimentaryelement program, and using a mathematical operation such as subtraction,determine if a nutrient deficiency exists. Computing device 104 maymatch a compatible alimentary element to address the deficiency.Applying the nutrient quantity of an alimentary element may result in a‘nutrient surplus’ in which the timing of a subsequent meal may beextended, or a different alimentary element selected altogether.Applying the nutrient quantity may indicate a ‘nutrient deficiency’ inwhich case a second alimentary element may be selected to increase thenutrient amount. The quantity may indicate all nutrients are withinthreshold numerical value ranges, in which case the subject may not needto consume anything for a time.

Continuing in reference to FIG. 1, computing device may calculate achange in the nutrient profile 112 as a function of timing thecompatible alimentary element. Computing device 104 may accept an inputof a starting value in nutrient profile 112 and a second input of thenutrition facts data of a compatible alimentary element. Computingdevice 104 may then use the trained nutrient machine-learning model 116to generate an output of an updated nutrient profile 112. The model maycontain relationships regarding the pharmacokinetics of nutrientabsorption of macromolecules, micronutrients, etc. The updated nutrientprofile 112, which may reflect changes in nutrient levels afterconsumption, may show nutrients that were obtained, not obtained, an/ordepleted. The resulting updated nutrient profile 112 data from using thetrained model and the inputs may then inform nourishment consumptionprogram 120, re-calculating the timing of the next meal as nutrientlevels change. Persons skilled in the art may appreciate that system 100may iteratively update nutrient profile 112 at defined intervals, forinstance (5 times daily; once an hour, after a meal is eaten), to informmeal timing.

Continuing in reference to FIG. 1, calculating a change in the nutrientprofile 112 as a function of timing the compatible alimentary elementmay include calculating differences in individual nutrient levels insubject. For instance, nutrient profile 112 may include essential aminoacid levels for subject as a function of how much protein they haveconsumed and the protein sources. Computing device 104 may quantifychanges in essential amino acid levels after lunch, and use thatcalculation to best time, to achieve daily recommended amino acidlevels, especially for branch chain amino acids (BCAAs). Computingdevice 104 may select alimentary elements for timing use a mathematicaloperation, such as addition or subtraction, for instance by adding theamount of protein per serving to the current nutrient levels. Computingdevice 104 may optimize timing by using a system of equations and/ormathematical expressions to calculate rates (or velocity) of change inthe nutrient levels as a function of time. In such an example, the firstderivative may be the velocity of reaction (metabolism), secondderivative is acceleration (nutrient absorption), and third derivativeis the ‘jerk’, or rate of change of acceleration. The third derivativemay refer to ‘how long nutrients will continue to increase prior toneeding next meal’ after eating. Computing device 104 may employ avariety of methods to calculate a change based on relationshipsidentified by nutrient machine-learning model 116. For instance, amulti-variable system of equations, a matrix, vector analysis, series offunctions, transforms, derivatives, and the like, may be discovered bynutrient machine-learning model 116 for mapping how nutrient levelschange over time, or react to eating a meal, with sufficientphysiological data.

Continuing in reference to FIG. 1, calculating a change in the nutrientprofile 112 as a function of timing the compatible alimentary elementmay include calculating differences in individual nutrient levels insubject Timing may be affected by physiological data regarding fitnessdata, for instance from a fitness tracking application, wearable device,etc.

Continuing in reference to FIG. 1, nourishment consumption program 120may include a queue of a plurality of compatible alimentary elements,wherein each compatible alimentary element includes an identifier. An“identifier,” as used in this disclosure, is a datum of identifiableinformation relating to a compatible alimentary element. An identifier124 may include alimentary element name, serving size, price,distributor, restaurant identity, nutrition facts, among otheridentifiable information for an alimentary element. An identifier 124may include information necessary for ordering compatible alimentaryelement, for instance via a mobile application, a web browser, and theInternet etc. An identifier 124 may include information detailing theidentity of a compatible alimentary element and perhaps why it isnecessary for the subject, how it may improve health, etc. A “queue,” asused in this disclosure, is a collection of elements that are maintainedin a sequence and can be modified by the addition of entities andremoval of elements from the sequence. In non-limiting illustrativeexamples, the queue may have an “active end” and a “reserve end,”wherein the active end is the ‘most appropriate compatible alimentaryelement and time of consuming’ to be displayed such as by timing, orsome other discriminating criteria; additionally, there may be relatedalimentary elements that are in the queue “behind” the first active endalimentary element and alternatives nearer the reserve end. In furthernon-limiting illustrative examples, a user may indicate via a graphicaluser interface that they do not want an alimentary element, wherebycomputing device 104 may remove it from the active end and push up byone place the next alimentary elements in the queue. In such an example,computing device 104 may add a newly generated alimentary element to thereserve end to maintain a list that a user may view, scroll through,select/deselect, or the like.

Continuing in reference to FIG. 1, nourishment consumption program 120may include a time associated with the identifier. A “time,” as used inthis disclosure, is a datum of chronologically identifiable data usedfor communicating the timing of consumption. The time may include a timeof day, a countdown, or elapsed time for the next alimentary element.Time may include a dynamic time counter, which visibly changes as afunction of nutritional input; alternatively or additionally, time mayinclude predetermined times, such as a schedule linked to a calendar. Innon-limiting illustrative examples, since each compatible alimentaryelement may impose a different effect on nutrient profile 112, theidentity of each may include a unique time table for consumption.

Continuing in reference to FIG. 1, nourishment consumption program 120may include a nutrient quantifier for adjusting the nutrient profile 112as a function of consumption of an alimentary element associated withthe identifier. A “nutrient quantifier,” as used in this disclosure, isa metric, or instruction, that includes an effect on nutrition for analimentary element. A nutrient quantifier 128 may include numericalvalues, for instance, as to which nutrients should decrease afterconsuming. A nutrient quantifier may include a series of values as afunction, for instance, which details how a series of nutrient levelschange over time after consuming a particular alimentary element, suchas blood sugar and sodium change over a 6 hour period from consuming acheeseburger. A nutrient quantifier 128 may include an instruction, orlogical rule, which dictates how the nutrient profile 112 should bemodified from each alimentary element consumed, including how to changethe time (to next meal), and which alimentary element identifiers shouldbe added/removed (from queue). Nutrient quantifier 128 may include, innon-limiting illustrative examples, that eating a particular snack+1hour after consuming a first meal may extend the timing for a secondmeal for another 3 hours, and perhaps change the identity of the secondmeal, depending on what the snack provides.

Continuing in reference to FIG. 1, nourishment consumption program 120may modify identifier 124, time, and nutrient quantifier 128 based onevolutionary considerations such as circadian rhythm, among otherconsiderations. Circadian rhythms are self-sustained approximately24-hour oscillations in behavior, physiology, and metabolism. Theserhythms have evolved to permit organisms to effectively respond to thepredictable daily change in the light:dark cycle and the resultantrhythms in food availability encountered in nature. Genetic, epigenetic,biochemical, and physiological studies have revealed more than 10% ofexpressed genes in any organ exhibit circadian oscillation, and this isseen in liver metabolism, musculoskeletal tissue metabolism, appetitecontrol, blood panel results, etc. These rhythmic transcripts encode keyrate-determining steps in neuroendocrine, signaling, and metabolicpathways. Such regulation temporally separates cellular processes andoptimizes cellular and organismal fitness. Although the circadian clockis cell-autonomous and has been identified in the majority of tissuetypes, the circadian system is organized in a hierarchical manner inwhich the hypothalamic suprachiasmatic nucleus (SCN) of the hypothalamusfunctions as the master circadian clock (also regulating appetitecontrol) that uses both diffusible and synaptic mechanisms toorchestrate circadian rhythms in the peripheral organs at appropriatephase. For instance, photoreceptive retinal ganglion cells (lightharvesting system) send ambient light information to the SCN throughmonosynaptic connection to ensure that the circadian system is entrainedto the daily light:dark cycle. This circadian que, among numerousothers, may be reflected in a physiological data 108, for system 100 toaccurately determine per-subject circadian rhythm. Nutrient profile 112may include circadian rhythm dietary patterns, eating timing, sleepcycles, etc. For instance, nutritional input data of subject may be usedas an input to determine nourishment consumption program 120 timing,wherein patterns in nutritional input (when a subject eats) may helpidentify circadian rhythm consumption patterns. Such consumptionpatterns, reflected in nutrient profile 112, may assist in determiningpersonalized, highly accurate times of day for optimized consumption.Nourishment consumption program 120 may include identifier 124, time,and nutrient quantifier 128, that is based on a circadian rhythm asdetailed from nutrient profile 112 of a subject; nutrientmachine-learning model 116 may identify and describe relationships intraining data that capture per-subject circadian rhythm model ofnutrition.

Continuing in reference to FIG. 1, nourishment consumption program 120may include identifier 124, time, and nutrient quantifier 128 based oncultural considerations such as the standard 3-meal day, diet types anddieting fads, among other considerations. Typical breakfast, lunch, anddinner meals may be difficult to distinguish because skipping meals,snacking, and erratic eating patterns have become more prevalent. Sucheating styles may have various effects on cardiometabolic healthmarkers, namely obesity, lipid profile, insulin resistance, bloodpressure, heart rate, VO₂ max, etc. Nourishment consumption program 120may include nutrient quantifiers 128 and times aimed at variousconsumption models such as ‘skipping breakfast’, ‘intermittent fasting’,‘decreasing meal frequency’ (number of daily eating occasions) andmodify timing of consumption based on these paradigms. Consumptionpatterns may be detailed in nutrient profile 112 or may be collected asinputs via a subject interaction with a user device, such as via aquestionnaire provided by a graphical user interface. Furthermore,nourishment consumption program 120 may include programdefinitions/instructions for meals, snacks, and consumption for use inidentifying per-subject consumption patterns to refine nourishmenttiming more accurately.

Continuing in reference to FIG. 1, providing the nourishment consumptionprogram 120 may include generating, via a graphical user interface, arepresentation of the nourishment consumption program 120. A “graphicaluser interface,” as used in this disclosure, is any form of a userinterface that allows a subject to interface with an electronic devicethrough graphical icons, audio indicators, text-based interface, typedcommand labels, text navigation, and the like, wherein the interface isconfigured to provide information to the subject and accept input fromthe subject. Graphical user interface may accept subject input, whereinsubject input may include an interaction (such as a questionnaire) witha user device. A user device may include computing device 104, a“smartphone,” cellular mobile phone, desktop computer, laptop, tabletcomputer, internet-of-things (JOT) device, wearable device, among otherdevices. User device may include any device that is capable forcommunicating with computing device 104, database, or able to receive,transmit, and/or display nutrient profile 112, nourishment consumptionprogram 120, compatible alimentary elements, etc., for instance via adata network technology such as 3G, 4G/LTE, 5G, Wi-Fi (IEEE 802.11family standards), and the like. User device may include devices thatcommunicate using other mobile communication technologies, or anycombination thereof, for short-range wireless communication (forinstance, using Bluetooth and/or Bluetooth LE standards, AirDrop, Wi-Fi,NFC, etc.), and the like.

Continuing in reference to FIG. 1, providing a representation of thenourishment consumption program 120 may include providing an audiovisualnotification. An “audiovisual notification,” as used in this disclosure,is an audio and/or visual based notification that may be displayed viaan interface with computing device 104. An audiovisual notification mayinclude a prompt to order an alimentary element. An audiovisualnotification may include a compatible alimentary element from analimentary element program. Providing a representation of thenourishment consumption program 120 may include linking, for instance, asubject's calendar application with an alimentary element program. Innon-limiting illustrative examples, a user device may be configured toset timed reminders for subject to consume foods, where foods aredetermined as a function of current location, options, etc.

Continuing in reference to FIG. 1, after providing the nourishmentconsumption program 120, computing device 104 may update the nutrientprofile 112 as a function of subject nutrient consumption. Definedintervals for updating nutrient profile 112 may be set by computingdevice 104 using reactive computing. “Reactive computing,” as used inthis disclosure is a declarative programming paradigm that is concernedwith data streams and the propagation of change in such data oversampled time period. Reactive computing may also be referred to as“reactive programming.” Reactive computing may be used to iterativelysample data inputs and, according to an internal “clock”, generateiterative outputs in real-time, as the input data is collected. As usedin this disclosure, input data may include nutritional input and/orphysiological data 108, for instance as input by subject or collected bya physiological sensor, respectively, and received by computing device104. As used in this disclosure, output data may include nutrientprofile 112 and nourishment consumption program 120. Input data mayinclude data that is generated as training data, and outputs may be froma machine-learning process. Computing device 104 may be configured,using reactive computing, to express static (such as arrays) and/ordynamic (such as event emitters) “data streams” with relative ease, andalso communicate an inferred dependence within the associated “executionmodel” which facilitate the automatic propagation of the changed dataflow. For instance, computing device 104 may be configured to employ atrained machine-learning process or model, which describes amathematical relationship between a particular input to a particularoutput as the “execution model” to automatically propagate outputs formthe incoming signal data. Essentially, computing device 104 may usereactive computing to iteratively receive nutritional inputs and/orphysiological data 108 (inputs) and generate nutrient profile 112 andnourishment consumption program 120 (outputs) at regular scheduledintervals, including as data is received (real-time), according totrained machine-learning models such as the nutrient machine-learningmodel 116. Computing device 104 may use the nutrient profile 112 and analimentary element program stored in database to generate nourishmentconsumption program 120 each time nutrient profile 112 is updatedthroughout the subject's day.

Continuing in reference to FIG. 1, reactive computing may include“model-view-controller” (MVC) architecture, wherein reactive programmingmay facilitate changes in an underlying model that are reflectedautomatically in an associated view. For instance, a trained nutrientmachine-learning model 116, which may correlate physiological data 108to nutrient profile 112 data, wherein the nutrient profile 112 mayinclude numerical values for each nutrient. Reactive computing may beperformed by computing device 104 using reactive extensions, such asRxJs, RxJAva, RxPy, RxSwift, and other APIs. Reactive computing may beimplemented using any type of change propagation algorithm, such as apull, push, and/or push-pull type approach to data propagation. Reactivecomputing may be any object-oriented reactive programming (OORP),functional reactive programming (FRP), or the like. Reactive programmingmay be implemented using for instance rule-based reactive programminglanguages such as through using relation algebra with Ampersand, Elm,and/or Observable. Persons skilled in the art, upon review of thisdisclosure in its entirety, will be aware of the various ways in whichto implement reactive computing to sample inputs and provide updates inreal-time, or at any defined interval.

Referring now to FIG. 2, a non-limiting exemplary embodiment ofnourishment consumption program 120 is illustrated. Nourishmentconsumption program 120 may include several consumption patterns 204,for instance as designated ‘A’, ‘B’, and ‘C’. Each may include thetiming and designation of meals, snacks, caloric content, etc.Consumption patterns 204 may include daily patterns of nourishmentconsumption. Consumption patterns may be organized into consumptionschedule 208, such as a monthly schedule. Nourishment consumptionprogram 120 may include a variety of consumption patterns 204 organizedinto consumption schedules 208, for instance based on physiologicalgoals such as lowering BMI, fighting obesity, ameliorating a particulardisease (type-2 diabetes), or addressing a symptom (improving sleepdeprivation). Nourishment consumption program 120 may include a varietyof consumption patterns 204 organized into consumption schedules 208with particular identified alimentary elements, such as particulargrains, meats, fruits, diary, vegetables, and the like, arranged indietary paradigms such as ‘ketogenic diet’, ‘low glycemic index diet’,‘plant-based diet’, etc., where the timing of nourishment is guidedtoward a goal. Such a goal may include maintaining a certain level ofiron in the body or keeping cholesterol or blood sugar within aparticular range.

Referring now to FIG. 3, a non-limiting exemplary embodiment ofnourishment consumption program 120 provided on a user device isillustrated. User device 304 may include computing device 104, a“smartphone,” cellular mobile phone, desktop computer, laptop, tabletcomputer, internet-of-things (JOT) device, wearable device, among otherdevices. User device may include any device that is capable forcommunicating with computing device 104, database, or able to receive,transmit, and/or display nutrient profile 112, nourishment consumptionprogram 120, compatible alimentary elements 308. User device 304 mayprovide a nutrient profile 112, for instance as a collection of metricsdetermined from physiological data 108 data. User device may providedata concerning average levels of nutrients, nutrient lows, nutrienthighs, etc. User device may link timing of foods to preemptive orderinginterface for ordering an alimentary element, for instance through adesignated mobile application, mapping tool or application, etc. Userdevice may link nourishment consumption program 120 to a schedulingapplication, such as a ‘calendar’ feature on user device, which may settimers, alarms, and the like.

Referring now to FIG. 4, a non-limiting illustrative embodiment ofnutrient profile 112 data is illustrated. Physiological data 108 mayinclude data such as blood concentration of nutrients (mg/dL). Computingdevice 104 may receive physiological data 108, including nutritionalinput, such as times of meals consumed, and nutrition facts ofalimentary elements consumed. Nutrient machine-learning process 116 maydetermine relationships between consumption of particular alimentaryelements and the concentration of nutrient in a physiological data. Forinstance, as depicted in FIG. 4, nutrient #1 may correspond to bloodsugar, which generally oscillates between 50 and 140 mg/dL throughoutthe day for healthy adults. From time point ‘0 hour’, or 8 am for whensubject awakens to approximately ‘+10 hours’ blood sugar remains innormal nutrient threshold values. At approximately, ‘+10 hours’ time,the subject consumes the largest meal of the day, which is diner atapproximately 6 pm, and then consumes a nutrient-rich alimentary elementapproximately 1.5 hours later prior to bed leading to blood sugarbetween 140 and 199 mg/dL, implying prediabetes without optimal mealsand timing. A plurality of nutrients may reach local maxima nutrientamounts during digestion in the evening-night (14-18 hours) if thelargest amount of nutrients are consumed during the last few hoursawake. Such a pattern may suggest eating smaller meals or spreadingnutrients out over a longer time. Inflection points in the function maymatch to timing of meals. The period after the infection (+1 hour postmeal, +2 hours, etc.) may be used to train a machine-learning model todetermine rates associated with absorption and metabolism with thatsubject. Persons skilled in the art may appreciate that performed oversufficiently large periods, and with a large variety of alimentaryelements, for a large set of nutrients, nutrient machine-learning model116 may accurately determine nutrient profile 112 metrics, parameters,numerical values, etc.

Referring now to FIG. 5, an exemplary embodiment of a machine-learningmodule 500 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 504 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 508 given data provided as inputs 512;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a subject andwritten in a programming language.

Still referring to FIG. 5, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 504 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 504 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 504 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 504 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 504 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 504 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data504 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 5,training data 504 may include one or more elements that are notcategorized; that is, training data 504 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 504 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 504 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailherein. Training data 504 used by machine-learning module 500 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 5, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailherein; such models may include without limitation a training dataclassifier 516. Training data classifier 516 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedherein, such as a mathematical model, neural net, or program generatedby a machine learning algorithm known as a “classification algorithm,”as described in further detail herein, that sorts inputs into categoriesor bins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 500 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 504. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 516 may classify elements of training data to elementsthat characterizes a sub-population, such as a subset of physiologicaldata 108 (such as gene expression patterns as it relates to nutrientprofile 112) and/or other analyzed items and/or phenomena for which asubset of training data may be selected.

Still referring to FIG. 5, machine-learning module 500 may be configuredto perform a lazy-learning process 520 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofpredictions may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 504. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 504elements, such as classifying physiological data 108 elements nutrientprofile 112 elements and assigning a value as a function of some rankingassociation between elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail herein.

Alternatively or additionally, and with continued reference to FIG. 5,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 524. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 524 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 524 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 504set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. A machine-learning model may be used to derivenumerical scales for providing numerical values to nutrient profile 112,as described above, to “learn” the upper and lower limits to thenumerical scale, the increments to providing scoring, and the criteriafor increasing and decreasing elements encompassed in the nutrientprofile 112. A machine-learning model may be used to “learn” whichelements of physiological data 108 have what effect on nutrient profile112, and which elements of nutrient profile 112 are effect by particularalimentary elements and the magnitude of effect, etc.

Still referring to FIG. 5, machine-learning algorithms may include atleast a supervised machine-learning process 528. At least a supervisedmachine-learning process 528, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude a nutrient profile 112 (potentially classified into categories),as described above as inputs, nourishment consumption program 120outputs, and a ranking function representing a desired form ofrelationship to be detected between inputs and outputs; ranking functionmay, for instance, seek to maximize the probability that a given input(such as a recommended daily value or dietary goal) and/or combinationof elements inputs is associated with a given output (time of day toconsume a meal) to minimize the probability that a given input is notassociated with a given output, for instance finding the most suitabletimes to consume meals, and what the meals should be. Ranking functionmay be expressed as a risk function representing an “expected loss” ofan algorithm relating inputs to outputs, where loss is computed as anerror function representing a degree to which a prediction generated bythe relation is incorrect when compared to a given input-output pairprovided in training data 504. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process528 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 5, machine learning processes may include atleast an unsupervised machine-learning process 532. An unsupervisedmachine-learning process 532, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process 532 may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 5, machine-learning module 500 may be designedand configured to create a machine-learning model 524 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 5, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 5, models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training data 504 set are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data 504.

Referring now to FIG. 6, a non-limiting exemplary embodiment 600 of anourishment program database 604 is illustrated. Physiological data 108for a plurality of subjects, for instance for generating a training dataclassifier 516, may be stored and/or retrieved in nourishment programdatabase 604. Physiological data 108 data from a plurality of subjectsfor generating training data 504 may also be stored and/or retrievedfrom a nourishment program database 604. Computing device 104 mayreceive, store, and/or retrieve training data 504, wearable device data,physiological sensor data, and the like, from nourishment programdatabase 604. Computing device 104 may store and/or retrieve nutrientmachine-learning model 116, among other determinations, I/O data,models, and the like, in nourishment program database 604.

Continuing in reference to FIG. 6, nourishment program database 604 maybe implemented, without limitation, as a relational database, akey-value retrieval database such as a NOSQL database, or any otherformat or structure for use as a database that a person skilled in theart would recognize as suitable upon review of the entirety of thisdisclosure. Nourishment program database 604 may alternatively oradditionally be implemented using a distributed data storage protocoland/or data structure, such as a distributed hash table and the like.Nourishment program database 604 may include a plurality of data entriesand/or records, as described above. Data entries in a nourishmentprogram database 604 may be flagged with or linked to one or moreadditional elements of information, which may be reflected in data entrycells and/or in linked tables such as tables related by one or moreindices in a relational database. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which data entries in a database may store, retrieve, organize,and/or reflect data and/or records as used herein, as well as categoriesand/or populations of data consistent with this disclosure.

Further referring to FIG. 6, nourishment program database 604 mayinclude, without limitation, physiological data table 608, nutrientprofile table 612, nourishment consumption program table 616, timeschedule table 620, alimentary element table 624, and/or heuristic table628. Determinations by a machine-learning process, machine-learningmodel, ranking function, and/or classifier, may also be stored and/orretrieved from the nourishment program database 604. As a non-limitingexample, nourishment program database 604 may organize data according toone or more instruction tables. One or more nourishment program database604 tables may be linked to one another by, for instance in anon-limiting example, common column values. For instance, a commoncolumn between two tables of nourishment program database 604 mayinclude an identifier of a submission, such as a form entry, textualsubmission, accessory device tokens, local access addresses, metrics,and the like, for instance as defined herein; as a result, a search by acomputing device 104 may be able to retrieve all rows from any tablepertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofdata, including types of data, names and/or identifiers of individualssubmitting the data, times of submission, and the like; persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various ways in which data from one or more tables may belinked and/or related to data in one or more other tables.

Continuing in reference to FIG. 6, in a non-limiting embodiment, one ormore tables of an nourishment program database 604 may include, as anon-limiting example, a physiological data table 608, which may includecategorized identifying data, as described above, including geneticdata, epigenetic data, microbiome data, physiological data, and thelike. Physiological data table 608 may include physiological data 108categories according to metabolism, absorption, etc., categories, andmay include linked tables to mathematical expressions that describe theimpact of each physiological data 108 datum on nutrient profile 112, forinstance threshold values for gene expression, etc., as it relates tonutrient levels. One or more tables may include nutrient profile table612, which may include data regarding physiological data 108,thresholds, values, categorizations, and the like, that system 100 mayuse to calculate, derive, filter, retrieve and/or store current nutrientlevels, metabolic rates, absorption rates, digestive difficulties, andthe like. One or more tables may include nourishment consumption programtable 616, which may include data regarding times to eat, identifiers ofalimentary elements, schedules, diet types, and the like. Nourishmentconsumption program table 616 may include data from alike subjects withsimilar physiological data 108, and the like, that system 100 may use tocalculate, derive, filter, retrieve and/or store meal times, forinstance timing blood sugar based on meals as a function of alikesubjects' meal scheduling. One or more tables may include time scheduletable 620, which may include data including times of previousconsumption, future scheduled consumption, and the like, that system 100may use to link to nutrient profile 112 and/or nourishment consumptionprogram 120. One of more tables may include an alimentary element table624, which may include identifiers and times associated with alimentaryelements. One or more tables may include, without limitation, aheuristic table 628, which may organize rankings, scores, models,outcomes, functions, numerical values, scales, arrays, matrices, and thelike, that represent determinations, probabilities, metrics, parameters,values, and the like, include one or more inputs describing potentialmathematical relationships, as described herein.

Referring now to FIG. 7, an exemplary embodiment 700 of a method fortiming impact of nourishment consumption is illustrated. At step 705,computing device 104 is configured for a nutrient profile 112 of asubject, wherein the nutrient profile 112 maps physiological data of thesubject to current nutrient levels of the subject. Receiving thenutrient profile 112 may include training a nutrient machine-learningmodel 116 with training data that includes a plurality of data entrieswherein each entry correlates physiological data 108 to current nutrientlevels of the subject, and determining the nutrient profile as afunction of the nutrient machine-learning model; this may beimplemented, without limitation, as described above in FIGS. 1-6.

Still referring to FIG. 7, at step 710, computing device 104 isconfigured for determining, using the nutrient profile 112, anourishment consumption program 120, wherein the nourishment consumptionprogram includes at least an alimentary element, and a time of day forconsuming the alimentary element wherein the time of day is determinedas a function of the nutrient profile 112 and the current nutrient levelof the subject. Determining the nourishment consumption program 120 mayinclude retrieving an alimentary element program comprising compatiblealimentary elements. Determining the nourishment consumption program 120may include identifying a compatible alimentary element to address adatum of the nutrient profile 112. Computing device 104 may calculate achange in the nutrient profile 112 as a function of a time of day forconsuming the compatible alimentary element. Nourishment consumptionprogram 120 may include a queue of a plurality of compatible alimentaryelements, wherein each compatible alimentary element includes anidentifier. Nourishment consumption program 120 may include the time ofday associated with the identifier, wherein the time of day is selectedbased on the nutrient profile 112. Nourishment consumption program 120may include a nutrient quantifier for adjusting the nutrient profile 112as a function of consumption of an alimentary element associated withthe identifier; this may be implemented, without limitation, asdescribed above in FIGS. 1-6.

Continuing in reference to FIG. 7, at step 715, computing device 104 isconfigured for providing, to the subject, the nourishment consumptionprogram 120. Providing the nourishment consumption program 120 mayinclude generating, via a graphical user interface, a representation ofthe nourishment consumption program 120. Providing the nourishmentconsumption program 120 may include updating the nutrient profile 112 asa function of subject nutrient consumption; this may be implemented,without limitation, as described above in FIGS. 1-6.

Continuing in reference to FIG. 7, at step 720, computing device 104 isconfigured for providing, to the subject, the nourishment consumptionprogram 120. Providing the nourishment consumption program 120 mayinclude generating, via a graphical user interface, a representation ofthe nourishment consumption program 120. After providing the nourishmentconsumption program, computing device 104 may update the nutrientprofile 112 as a function of subject nutrient consumption; this may beimplemented, without limitation, as described above in FIGS. 1-6.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC)

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for timing impact of nourishmentconsumption, the system comprising: a computing device, the computingdevice configured to: receive a nutrient profile of a subject, whereinthe nutrient profile maps physiological data of the subject to currentnutrient levels of the subject, and wherein the nutrient profilecomprises pharmacokinetics for a plurality of nutrients; determine,using the nutrient profile, a nourishment consumption program, whereinthe nourishment consumption program includes: at least an alimentaryelement; and a time of day for consuming the alimentary element whereinthe time of day is determined as a function of the nutrient profile andthe current nutrient level of the subject; provide, to the subject, thenourishment consumption program; receive a first set of nutritionconsumption data of the subject; train a nutrient machine-learning modelusing a first set of training data that includes the first set ofnutrition consumption data and the nutrient profile as inputs; generate,using the nutrient machine learning model, a first updated nutrientprofile as an output; determine, using the first updated nutrientprofile, a first updated nourishment consumption program; provide, tothe subject, the first updated nourishment consumption program; receivea second set of nutrition consumption data of the subject; train thenutrient machine-learning model using a second set of training data thatincludes the second set of nutrition consumption data and the firstupdated nutrient profile as inputs; generate, using the nutrient machinelearning model, a second updated nutrient profile as an output;determine, using the second updated nutrient profile, a second updatednourishment consumption program; and provide, to the subject, the secondupdated nourishment consumption program, wherein at least one of thefirst updated nutrient profile and the second updated nutrient profilecomprises pharmacokinetics for a plurality of nutrients.
 2. The systemof claim 1, wherein training the nutrient machine-learning modelcomprises iteratively and automatedly training the nutrientmachine-learning model.
 3. The system of claim 2, wherein providing theupdated nourishment consumption programs comprises iteratively andautomatedly providing the updated nourishment consumption programs atscheduled intervals.
 4. The system of claim 1, wherein determining thenourishment consumption program further comprises identifying acompatible alimentary element to address a datum of the nutrientprofile.
 5. The system of claim 4, wherein the computing devicecalculates a change in the nutrient profile as a function of a time ofday for consuming the compatible alimentary element.
 6. The system ofclaim 1, wherein the nourishment consumption program includes a queue ofa plurality of compatible alimentary elements, wherein each compatiblealimentary element includes an identifier.
 7. The system of claim 6,wherein the nourishment consumption program includes the time of dayassociated with the identifier, wherein the time of day is selectedbased on the nutrient profile.
 8. The system of claim 6, wherein thenourishment consumption program includes a nutrient quantifier foradjusting the nutrient profile as a function of consumption of analimentary element associated with the identifier.
 9. The system ofclaim 1, wherein providing the nourishment consumption program furthercomprises generating, via a graphical user interface, a representationof the nourishment consumption program.
 10. The system of claim 1,wherein determining the nourishment consumption program furthercomprises retrieving an alimentary element program comprising compatiblealimentary elements.
 11. A method for timing impact of nourishmentconsumption, the method comprising: receiving, by a computing device, anutrient profile of a subject, wherein the nutrient profile mapsphysiological data of the subject to current nutrient levels of thesubject; determining, by the computing device, using the nutrientprofile, a nourishment consumption program, wherein the nourishmentconsumption program includes: at least an alimentary element; and a timeof day for consuming the alimentary element wherein the time of day isdetermined as a function of the nutrient profile and the currentnutrient level of the subject; providing, by the computing device, tothe subject, the nourishment consumption program; receiving, by thecomputing device, a first set of nutrition consumption data of thesubject; training, by the computing device, a nutrient machine-learningmodel using a first set of training data that includes the first set ofnutrition consumption data and the nutrient profile as inputs;generating, by the computing device, using the nutrient machine learningmodel, a first updated nutrient profile as an output; determining, bythe computing device, using the first updated nutrient profile, a firstupdated nourishment consumption program; providing, by the computingdevice, to the subject, the first updated nourishment consumptionprogram; receiving, by the computing device, a second set of nutritionconsumption data of the subject; training, by the computing device, thenutrient machine-learning model using a second set of training data thatincludes the second set of nutrition consumption data and the firstupdated nutrient profile as inputs; generating, by the computing device,using the nutrient machine learning model, a second updated nutrientprofile as an output; determining, by the computing device, using thesecond updated nutrient profile, a second updated nourishmentconsumption program; and providing, by the computing device, to thesubject, the second updated nourishment consumption program, wherein atleast one of the first updated nutrient profile and the second updatednutrient profile comprises pharmacokinetics for a plurality ofnutrients.
 12. The method of claim 11, wherein training the nutrientmachine-learning model comprises iteratively and automatedly trainingthe nutrient machine-learning model.
 13. The method of claim 12, whereinproviding the updated nourishment consumption programs comprisesiteratively and automatedly providing the updated nourishmentconsumption programs at scheduled intervals.
 14. The method of claim 11,wherein determining the nourishment consumption program furthercomprises identifying a compatible alimentary element to address a datumof the nutrient profile.
 15. The method of claim 4, wherein thecomputing device calculates a change in the nutrient profile as afunction of a time of day for consuming the compatible alimentaryelement.
 16. The method of claim 11, wherein the nourishment consumptionprogram includes a queue of a plurality of compatible alimentaryelements, wherein each compatible alimentary element includes anidentifier.
 17. The method of claim 16, wherein the nourishmentconsumption program includes the time of day associated with theidentifier, wherein the time of day is selected based on the nutrientprofile.
 18. The method of claim 16, wherein the nourishment consumptionprogram includes a nutrient quantifier for adjusting the nutrientprofile as a function of consumption of an alimentary element associatedwith the identifier.
 19. The method of claim 11, wherein providing thenourishment consumption program further comprises generating, via agraphical user interface, a representation of the nourishmentconsumption program.
 20. The method of claim 11, wherein determining thenourishment consumption program further comprises retrieving analimentary element program comprising compatible alimentary elements.